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Translating Data Science to Business Value

Posted Mar 03
# Applied AI
# Tech Talk
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SPEAKER
Keegan Hines
Keegan Hines
Keegan Hines
Vice President of Machine Learning @ Arthur

Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor in the Data Science Program at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.

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Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor in the Data Science Program at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.

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SUMMARY

AI offers the potential to solve our most complex business problems; but to truly deliver on this potential, enterprises need to rethink core operating principles and build new practices that translate data science to business value. In this session, I will share some tough lessons learned over the years when trying to bring data science projects into production and truly impact business value. I'll close by introducing a step-by-step framework for accelerating the use of ML to solve business problems. From building a business case to measuring and optimizing performance in production, I'll share specific case studies across industries with actionable insights.

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